ctrl-freeq 0.3.0

Quantum gate and pulse design with optimal control

ctrl-freeq

Ctrl-freeq is a numerical framework for the design of quantum gates and pulses via optimal control theory, implemented in Python with PyTorch. The control pulse optimization problem is formulated and solved using automatic differentiation, and the resulting software provides both a programmatic API and a graphical user interface for the configuration and execution of optimization workflows. Multiple physical platforms are supported through a Hamiltonian model abstraction layer, including spin-chain systems and superconducting (transmon) qubits.

• PyPI package: ctrl-freeq
• Import path: ctrl_freeq
• Python: 3.11–3.13
• CLI entry point: freeq-gui

Features

The framework supports two physical platforms—spin chains and superconducting (transmon) qubits—through a Hamiltonian model abstraction layer. The optimizer works with the standard bilinear control formulation H(t) = H_drift + Σ_k u_k(t) · H_ctrl_k, which is platform-agnostic. Gate optimization problems are defined through JSON configuration files, and a high-level Python API is provided for loading configurations, executing the optimizer, and post-processing results. A Tkinter-based GUI (launched via freeq-gui) offers an interactive alternative with dynamic field relabelling and platform-aware gate selection. Optimization results may be visualized through interactive Plotly dashboards exported as standalone HTML files. CPU thread management is handled automatically, and optional GPU acceleration is available via CUDA.

Installation

Ctrl-freeq depends on NumPy, PyTorch, Qiskit, pytorch-minimize, Plotly, and other scientific packages.

# Install from source
uv sync

# Or with all development tools
uv sync --all-groups

Note: Dependency groups (dev, docs, tools) use PEP 735 and require uv — they are not pip extras.

For pip users:

pip install .

A detailed treatment of all installation methods is provided in the Installation documentation.

Quick Start

Launch the GUI

freeq-gui

Execution of this command launches the Tkinter-based graphical interface defined in ctrl_freeq/cli.py.

Use the API

from ctrl_freeq.api import CtrlFreeQAPI, run_from_config, load_single_qubit_config

# Load a built-in example configuration
config = load_single_qubit_config()

# Option A: one-shot helper
result = run_from_config(config)

# Option B: create an API instance to inspect and edit parameters
api = CtrlFreeQAPI(config)
print(api.get_config_summary())
api.update_parameter("optimization.max_iter", 500)
result = api.run_optimization()
print(f"Final fidelity: {api.parameters.final_fidelity:.6f}")

Project Structure

src/ctrl_freeq/       Core package
  api.py              High-level API
  cli.py              CLI entry point (freeq-gui)
  setup/              GUI and configuration setup
    hamiltonian_generation/
      base.py         HamiltonianModel ABC
      spin_chain.py   Spin-chain Hamiltonian
      superconducting.py  Transmon qubit Hamiltonian
  run/                Optimization engine
  utils/              Plotting, dashboards, helpers
examples/             Example configs and notebooks
tests/                Test suite
pyproject.toml        Build and packaging configuration

Development

Development tools are organized into dependency groups: dev (pytest, jupyter), docs (mkdocs), and tools (ruff, pre-commit).

# Install dev dependencies
uv sync --all-groups

# Run tests
uv run pytest -q

# Lint
uv run ruff format .
uv run ruff check --fix .

Compute Resource Selection (CPU/GPU)

Ctrl-freeq supports execution on both CPU and GPU (CUDA) hardware:

• cpu (default) — PyTorch threads are limited to max(1, os.cpu_count() - 1). This default may be overridden with the cpu_cores field.
• gpu — CUDA is used if available; the framework falls back to CPU with a warning if CUDA is not present.

API Configuration

config = {
    "compute_resource": "gpu",   # or "cpu" (default)
    "cpu_cores": 8,              # optional, CPU only
    ...
}

GUI

The compute resource may be selected from the Compute Resource dropdown in the Optimization Parameters section of the graphical interface.

Note: Only CUDA is supported for GPU acceleration. MPS is not currently supported.

A demonstration notebook is provided at examples/api_gpu_cpu_demo.ipynb, illustrating CPU vs CUDA selection and comparative timing.

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

Citation

If this software is used in academic work, please cite it using the metadata provided in CITATION.cff.